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56
Social Signal Processing: Survey of an Emerging Domain
, 2008
"... The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next- ..."
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Cited by 153 (32 self)
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The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing.
Toward a unified approach to statistical language modeling for Chinese
, 2001
"... This article presents a unified approach to Chinese statistical language modeling (SLM). Applying SLM techniques like trigram language models to Chinese is challenging because (1) there is no standard definition of words in Chinese; (2) word boundaries are not marked by spaces; and (3) there is a de ..."
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Cited by 75 (17 self)
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This article presents a unified approach to Chinese statistical language modeling (SLM). Applying SLM techniques like trigram language models to Chinese is challenging because (1) there is no standard definition of words in Chinese; (2) word boundaries are not marked by spaces; and (3) there is a dearth of training data. Our unified approach automatically and consistently gathers a high-quality training data set from the Web, creates a high-quality lexicon, segments the training data using this lexicon, and compresses the language model, all by using the maximum likelihood principle, which is consistent with trigram model training. We show that each of the methods leads to improvements over standard SLM, and that the combined method yields the best pinyin conversion result reported.
Uncertainty decoding for noise robust speech recognition
- in Proc. Interspeech
, 2004
"... This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration. It has not been submitted in whole or in part for a degree at any other university. Some of the work has been published previously in conference proceedings ..."
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Cited by 45 (12 self)
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This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration. It has not been submitted in whole or in part for a degree at any other university. Some of the work has been published previously in conference proceedings
Social Signal Processing: State-of-the-art and future perspectives of an emerging domain
- IN PROCEEDINGS OF THE ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA
, 2008
"... The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next- ..."
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Cited by 27 (7 self)
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The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes aset of recommendations for enabling the development of the next generation of socially-aware computing.
The use of clustering techniques for language modeling – application to Asian languages
- Computational Linguistics and Chinese Language Processing
, 2001
"... Abstract Cluster-based n-gram modeling is a variant of normal word-based n-gram modeling. It attempts to make use of the similarities between words. In this paper, we present an empirical study of clustering techniques for Asian language modeling. Clustering is used to improve the performance (i.e. ..."
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Cited by 18 (11 self)
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Abstract Cluster-based n-gram modeling is a variant of normal word-based n-gram modeling. It attempts to make use of the similarities between words. In this paper, we present an empirical study of clustering techniques for Asian language modeling. Clustering is used to improve the performance (i.e. perplexity) of language models as well as to compress language models. Experimental tests are presented for cluster-based trigram models on a Japanese newspaper corpus, and on a Chinese heterogeneous corpus. While the majority of previous research on word clustering has focused on how to get the best clusters, we have concentrated our research on the best way to use the clusters. Experimental results show that some novel techniques we present work much better than previous methods, and achieve up to more than 40% size reduction at the same perplexity
Correction of Disfluencies in Spontaneous Speech using a Noisy-Channel Approach
- in Proceedings of the 8th Eurospeech Conference
, 2003
"... In this paper we present a system which automatically corrects disfluencies such as repairs and restarts typically occurring in spontaneously spoken speech. The system is based on a noisy-channel model and its development requires no linguistic knowledge, but only annotated texts. Therefore, it has ..."
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Cited by 16 (4 self)
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In this paper we present a system which automatically corrects disfluencies such as repairs and restarts typically occurring in spontaneously spoken speech. The system is based on a noisy-channel model and its development requires no linguistic knowledge, but only annotated texts. Therefore, it has large potential for rapid deployment and the adaptation to new target languages. The experiments were conducted on spontaneously spoken dialogs from the English VERBMOBIL corpus where a recall of 77.2% and a precision of 90.2% was obtained. To demonstrate the feasibility of rapid adaptation additional experiments on the spontaneous Mandarin Chinese CallHome corpus were performed achieving 49.4% recall and 76.8% precision.
Anger recognition in speech using acoustic and linguistic cues
- Speech Communication
"... Abstract The present study elaborates on the exploitation of both linguistic and acoustic feature modeling for anger classification. In terms of acoustic modeling we generate statistics from acoustic audio descriptors, e.g. pitch, loudness, spectral characteristics. Ranking our features we see that ..."
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Cited by 11 (7 self)
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Abstract The present study elaborates on the exploitation of both linguistic and acoustic feature modeling for anger classification. In terms of acoustic modeling we generate statistics from acoustic audio descriptors, e.g. pitch, loudness, spectral characteristics. Ranking our features we see that loudness and MFCC seems most promising for all databases. For the English database also pitch features are important. In terms of linguistic modeling we apply probabilistic and entropy-based models of words and phrases, e.g. Bag-of-Words (BOW), Term Frequency (TF), Term FrequencyInverse Document Frequency (TF.IDF) and the Self-Referential Information (SRI). SRI clearly outperforms vector space models. Modeling phrases slightly improves the scores. After classification of both acoustic and linguistic information on separated levels we fuse information on decision level adding confidences. We compare the obtained scores on three different databases. Two databases are taken from the IVR customer care domain, another database accounts for a WoZ data collection. All corpora are of realistic speech condition. We observe promising results for the IVR databases while the WoZ database shows overall lower scores. In order to provide comparability in between the results we evaluate classification success using the f1 measurement in addition to overall accuracy figures. As a result, acoustic modeling clearly outperforms linguistic modeling. Fusion slightly improves overall scores. With a baseline of approximately 60% accuracy and .40 f1-meaurement by constant majority class voting we obtain an accuracy of 75% with respective .70 f1 for the WoZ database. For the IVR databases we obtain approximately 79% accuracy with respective .78 f1 over a baseline of 60% accurracy with respective .38 f1.
Matrix Updates for Perceptron Training of Continuous Density Hidden Markov Models
"... In this paper, we investigate a simple, mistakedriven learning algorithm for discriminative training of continuous density hidden Markov models (CD-HMMs). Most CD-HMMs for automatic speech recognition use multivariate Gaussian emission densities (or mixtures thereof) parameterized in terms of their ..."
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Cited by 10 (5 self)
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In this paper, we investigate a simple, mistakedriven learning algorithm for discriminative training of continuous density hidden Markov models (CD-HMMs). Most CD-HMMs for automatic speech recognition use multivariate Gaussian emission densities (or mixtures thereof) parameterized in terms of their means and covariance matrices. For discriminative training of CD-HMMs, we reparameterize these Gaussian distributions in terms of positive semidefinite matrices that jointly encode their mean and covariance statistics. We show how to explore the resulting parameter space in CD-HMMs with perceptron-style updates that minimize the distance between Viterbi decodings and target transcriptions. We experiment with several forms of updates, systematically comparing the effects of different matrix factorizations, initializations, and averaging schemes on phone accuracies and convergence rates. We present experimental results for context-independent CD-HMMs trained in this way on the TIMIT speech corpus. Our results show that certain types of perceptron training yield consistently significant and rapid reductions in phone error rates. 1.
A fast online algorithm for large margin training of continuous density hidden markov models
- in Proceedings of Interspeech-2009
, 2009
"... We propose an online learning algorithm for large margin training of continuous density hidden Markov models. The online algorithm updates the model parameters incrementally after the decoding of each training utterance. For large margin training, the algorithm attempts to separate the log-likelihoo ..."
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Cited by 9 (3 self)
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We propose an online learning algorithm for large margin training of continuous density hidden Markov models. The online algorithm updates the model parameters incrementally after the decoding of each training utterance. For large margin training, the algorithm attempts to separate the log-likelihoods of correct and incorrect transcriptions by an amount proportional to their Hamming distance. We evaluate this approach to hidden Markov modeling on the TIMIT speech database. We find that the algorithm yields significantly lower phone error rates than other approaches—both online and batch—that do not attempt to enforce a large margin. We also find that the algorithm converges much more quickly than analogous batch optimizations for large margin training. Index Terms: hidden Markov models, online learning, large margin classification, discriminative training, automatic speech recognition 1.
Abstract Gaussian-selection-based non-optimal search for speaker identification
, 2005
"... Most speaker identification systems train individual models for each speaker. This is done as individual models often yield better performance and they permit easier adaptation and enrollment. When classifying a speech token, the token is scored against each model and the maximum a priori decision r ..."
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Cited by 6 (0 self)
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Most speaker identification systems train individual models for each speaker. This is done as individual models often yield better performance and they permit easier adaptation and enrollment. When classifying a speech token, the token is scored against each model and the maximum a priori decision rule is used to decide the classification label. Consequently, the cost of classification grows linearly for each token as the population size grows. When considering that the number of tokens to classify is also likely to grow linearly with the population, the total work load increases exponentially. This paper presents a preclassifier which generates an N-best hypothesis using a novel application of Gaussian selection, and a transformation of the traditional tail test statistic which lets the implementer specify the tail region in terms of probability. The system is trained using parameters of individual speaker models and does not require the original feature vectors, even when enrolling new speakers or adapting existing ones. As the correct class label need only be in the N-best hypothesis set, it is possible to prune more Gaussians than in a traditional Gaussian selection application. The N-best hypothesis set is then evaluated using individual speaker models, resulting in an overall reduction of workload.
